Papers by Zonglin Yang
Language Models as Inductive Reasoners (2024.eacl-long)
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| Challenge: | Inductive reasoning is a core component of human intelligence. |
| Approach: | They propose a task to induce natural language rules from natural language facts using natural language as representation for knowledge instead of formal language. |
| Outcome: | The proposed task surpasses baselines in both automatic and human evaluations. |
Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing (2023.findings-emnlp)
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| Challenge: | Existing discourse parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. |
| Approach: | They propose to introduce a task-aware paradigm to improve the versatility of the parser. |
| Outcome: | Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the proposed framework. |
Large Language Models for Automated Open-domain Scientific Hypotheses Discovery (2024.findings-acl)
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| Challenge: | Existing research on hypothetical induction is limited by the observation annotations in the dataset and the ground truth hypotheses are mostly commonsense knowledge. |
| Approach: | They propose a first dataset for social science academic hypotheses discovery using raw web corpus as observations and propose valid, useful scientific hypothese . they propose 'a multi-module framework' that includes feedback mechanisms to boost performance. |
| Outcome: | The proposed dataset generates valid, novel, and helpful scientific hypotheses, even new to humanity, using open-domain data and a web corpus as observations. |
MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory and Fine-Grained Scientific Hypothesis Discovery (2026.acl-demo)
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| Challenge: | Existing approaches to scientific hypothesis discovery operate autonomously with little to no human guidance. |
| Approach: | They propose a framework that empowers scientists to steer the generative process via explicit signals. |
| Outcome: | The proposed framework outperforms existing models in terms of quality and accuracy of the input signals. |
Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)
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| Challenge: | Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains. |
| Approach: | They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks. |
| Outcome: | The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data. |
ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)
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Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou
| Challenge: | Large language models have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. |
| Approach: | They propose a benchmark for evaluating large language models on a sufficient set of scientific discovery sub-tasks. |
| Outcome: | The proposed framework extracts critical components from papers across 12 disciplines with expert validation confirming its accuracy. |
T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation (2023.findings-acl)
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| Challenge: | Recent advances in text-to-image generative models have produced high quality images with a breakthrough of inference speed. |
| Approach: | They propose a text-to-image association test framework that quantifies implicit stereotypes between concepts and valence and those in images. |
| Outcome: | The proposed framework quantifies implicit stereotypes between concepts and valence and those in images. |
Improving Event Duration Prediction via Time-aware Pre-training (2020.findings-emnlp)
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| Challenge: | Understanding duration of event expressed in text is crucial task in NLP . current methods focus on developing features and cannot utilize external textual knowledge. |
| Approach: | They propose two models that incorporate external knowledge by reading temporal-related news sentences. |
| Outcome: | The proposed models outperform baseline models and capture duration information more accurately. |
End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion (2023.eacl-main)
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| Challenge: | Pretrained language models have been shown to store knowledge in their parameters and have achieved reasonable performance in knowledge-intensive tasks. |
| Approach: | They propose to provide retrieved passages that contain relevant knowledge as additional input to the commonsense knowledge base completion (CKBC) task. |
| Outcome: | The proposed framework generates more valid, informative, and novel knowledge than the state-of-the-art COMET model for commonsense knowledge base completion (CKBC) tasks. |
Finding the Pillars of Strength for Multi-Head Attention (2023.acl-long)
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| Challenge: | Recent studies have revealed some issues of Multi-Head Attention (MHA) e.g., redundancy and over-parameterization. |
| Approach: | They propose to train attention heads with a self-supervised group constraint to focus on an essential but distinctive feature subset. |
| Outcome: | The proposed method achieves significant performance gains on three well-established tasks while significantly compressing parameters. |